Traditional Problems Research Articles

Zelimhan Yakub şiirinde gelenek ve yeniliğin vahdeti

Azerbaijani poetry, which has a thousand-year history, has been constantly updated, accumulating rich poetic experience, enriching itself in form, content, size, form, figurative structure and literary art. Tradition and innovation have always followed each other in poetry, and the development of each period and stage reflected a new poetic thought. One of the poets who was influenced by minstrel (aşıq) poetry is Zelimkhan Yakub. Zelimkhan Yakub is a modern Azerbaijani poet and bard who grew up in the minstrel environment of Borchali and held the post of president of the Azerbaijan Minstrels Association. Zelimkhan Yakub, a master of the minstrel art and performing saz melodies, became famous in the Turkisch world for his poems written in the style of minstrel poetry. The poet’s poems in a pure and fluent language set out the genetic memory, ethnography - holidays, weddings, everyday life of the Azerbaijani people. “Epic of Liberation”, “Epic of Yunus Emre”, “Epic of Great Life”, “Epic of Huseyn Sarajli”, “Epic of Fifty Years” and others are among the poems of the poet with epic motives. Poems written by the poet were published in 13 volumes. The life and work of the poet were studied by such researchers as Musa Nebioglu, Azer Turan, Rustam Kamal, Elnare Akimova, Kadim Rufullayev, Emine Huseynova and Sebuhi Bayramov. This article examines the problem of tradition and innovation in the poetry of Zelimkhan Yakub. As a result of the analysis of the poems of Zelimkhan Yakub, it was established that the poet skillfully used folklore, classical poetry and modern poetic traditions, but was not completely influenced by any of them.

How Artificial Intelligence Enhances Economic Growth and Development

Abstract: Artificial Intelligence (AI) is reshaping the global economy by introducing innovative solutions to traditional problems, enhancing productivity, and creating new markets. This paper explores how AI contributes to economic growth through applications in diverse industries such as healthcare, finance, manufacturing, and logistics. It discusses AI's ability to drive decision-making, optimize resource allocation, and foster technological innovation. Additionally, the review considers labor market impacts, regulatory challenges, and ethical concerns, offering strategies for inclusive and sustainable AI-driven economic development. While the potential for AI is vast, navigating its risks requires collaboration between governments, businesses, and societies.

No new problem for the Trinity: A reply to Mooney

Abstract The Father is God, the Son is God, the Holy Spirit is God, but there is only one God – this is the traditional problem of the Trinity. Recently, a new problem for the doctrine of the Trinity has been developed. God is triune, the Son is God, but the Son is not triune – that’s the new Triunity Problem. In this article, I show that adopting any solution to a traditional problem makes it possible to solve the new problem without incurring additional costs. I distinguish possible types of solutions to the traditional problem and point out the costs involved. I then show how each of these solutions can be used and developed to solve the new problem.

Optimizing Agricultural Labour: An Examination of On-Demand Workforce Solutions for Seasonal Demands

To address the growing challenges in managing on-demand Labour within the agricultural sector, we introduce Agro Finder, an innovative application developed using Figma for design and flutter for frontend development and firebase for backend. This research presents a comprehensive overview of the current state of Labour management in agriculture and proposes Agro Finder as a solution tailored to the sector's unique needs. The app leverages real-time data and sophisticated algorithms to streamline the connection between farmers and Labourers, optimizing Labour allocation based on geographic location, skills, and availability. Further analysis reveals that Agro Finder significantly enhances Labour utilization efficiency, reducing downtime in agricultural operations. The app's intuitive design, supported by Figma, ensures a user-friendly experience, while the fronted, powered by Flutter, provides reliable performance. The integration of these technologies facilitates efficient Labour management, leading to increased productivity and operational efficiency in agriculture. The research findings underscore the potential of applying modern development tools and methodologies to solve traditional problems in agriculture. By harnessing the power of Figma and Flutter, Agro Finder sets a new standard in on- demand Labour management, offering valuable insights and practical solutions. This study provides a solid foundation for future advancements in agricultural Labour management and highlights the importance of technological innovation in enhancing agricultural productivity. Key Words: on-demand Labour, agricultural management, mobile application development, Figma, Flutter, Labour allocation, real-time data processing, Firebase.

The Impact Of-Problematic Learning Styles-On Learning Outcomes Of Cultural Diversity In My Country 1 Sub-Diameter 1 Unity

The Impact of Problematic Learning Styles on Learning Outcomes in My Country Cultural Diversity Teachers and Teaching Education Dissertation Pacuan University. 2566 This study is a quasi-experimental study using traditional and problem-based learning models to investigate how cultural diversity sub-factors affect learning outcomes in My Country State School in Bokor IV C 78 Problem-based learning models have more requirements than traditional control class learning problems based on the experimental class model compared to the model. Because the results of both samples are lower than the L table, they have a high N value, so to find out whether the sample <0.148 is distributed in normal class groups, the researcher conducted an additional normality test using the Liliefors test hypothesis test of 7.42843 is greater than 1.9944 in the table, therefore the alternative hypothesis is accepted and the null hypothesis H0 is rejected Based on the above findings, the problem-based learning model is more effective than the traditional approach. diversity in my country.

Scalable deep learning artificial intelligence histopathology slide analysis and validation.

Deep learning involves an artificial intelligence (AI) approach and has been shown to provide superior performance for automating image recognition tasks, as well as exceeding human capabilities in both time and accuracy. Histopathology diagnostics is one of the more popular challenges at the intersection of artificial intelligence, computer vision, and medicine. Developing methods to automatically detect and identify pathologies in digitized histology slides imposes unique challenges due to the large size of these images and the complexity of the features present in biological tissue. Most methods that are capable of human-level recognition in histopathology are tuned to a specific problem since the computational complexity exceeds that of traditional image classification problems. In the current study, a deep learning approach is developed and presented that can be trained to locate and accurately classify different types of pathologies in gigapixel digitized histology slides along with completing the binary disease classification for the entire image. The approach uses a novel pyramid tiling approach to take advantage of spatial awareness around the area to be classified, while maintaining efficiency and scalability for gigapixel images. The approach is trained and validated on a wide variety of tissue types (i.e., testis, ovary, prostate, kidney) and pathologies taken from an epigenetically altered histology study at Washington State University. The newly developed procedure was optimized and validated along with comparison and validation on public histology datasets. The current developed procedure was found to be optimal and more reproducible when compared to manual procedures, and optimal to previous protocols that used fragmented tissue or slide analysis. Observations demonstrate that the deep learning histopathology analysis is significantly more efficient and accurate than standard manual histopathology analysis.

Research on the Protection and Inheritance of Ethnic Minority Costumes From the Perspective of Intangible Cultural Heritage

Ethnic minority clothing refers to clothing with unique characteristics of ethnic minorities. Ethnic minorities have rich clothing culture and living habits, and their production techniques are diverse and unique. As traditional handicrafts, with the development of modern society, some traditional handicrafts face severe inheritance problems. Therefore, it is urgent to protect them and they have historical responsibilities. Of course, the value of inheritance is immeasurable. In this article, I take the characteristics of ethnic minority clothing as the starting point and propose specific protection and inheritance strategies, aiming to provide some valuable reference suggestions for the protection and inheritance of ethnic minority clothing.

A Temporal–Geospatial Deep Learning Framework for Crop Yield Prediction

With the rapid development of information technology, the demand for digital agriculture is increasing. As an important agricultural production topic, crop yield has always attracted much attention. Currently, artificial intelligence, particularly machine learning, has become the leading approach for crop yield prediction. As a result, developing a machine learning method that accurately predicts crop yield has become one of the central challenges in digital agriculture. Unlike traditional regression prediction problems, crop yield prediction has a significant time correlation. For example, weather data for each county show strong temporal correlations. Moreover, geographic information from different regions also impacts crop yield to a certain extent. For example, if a county’s neighboring counties have a good harvest, then this county is likely to have high yields as well. This paper introduces a novel hybrid deep learning framework that combines convolutional neural network (CNN), graph attention network (GAT) and long short-term memory (LSTM) modules to enhance prediction accuracy. Specifically, CNN is employed to extract the features from the input data for each county in each year. GAT is introduced to model the geographical relationships between neighboring counties, allowing the model to capture spatial dependencies more effectively. LSTM is used to extract the temporal information within many years. The proposed hybrid deep learning framework CNN-GAT-LSTM captures both the temporal and spatial relationships, thereby improving the accuracy of yield prediction. We conduct experiments on a nationwide dataset that includes data from 1115 soybean-producing counties in 13 states in the United States covering the years from 1980 to 2018. We evaluate the performance of our proposed CNN-GAT-LSTM model based on three metrics, namely root of the mean squared error (RMSE), R-squared (R2) and correlation coefficient (Corr). The experimental results demonstrate that the proposed model achieves significant performance improvements over the existing state-of-the-art model, with RMSE reduced by 5%, R2 improved by 6% and Corr enhanced by 4%.

Youth religiosity as a subject of philosophical reflection

The authors of the article consider the religiosity of youth as a social phenomenon that ensures the continuity of ethno-confessional traditions and values, the transformation of healthy faith and reason in the sphere of sociocultural life. The contradictions of traditional and modern, national and universal (mass culture) are established, which is reflected in spiritual and moral life, religiosity, as an opportunity to search for national identity. In this regard, this work aims at a social analysis of the traditional problem of faith and reason, the scientific understanding of which will provide an opportunity for the formation of healthy faith, tolerance, spiritual and moral improvement of the lifestyle of the population. The key objective of the research is the socio-political analysis of the new meaning of spiritual and cultural life in the context of faith and reason as a prevention of religious puritanism and extremism. The authors sought to show the reflection of constitutional reforms in the principles of a social, secular, democratic state in Uzbekistan and their implementation in the context of human rights and freedom of conscience as conditions for preventing religious extremism. The religiosity of the peoples of the East is largely connected with the warning about the Day of Judgment, which is reminded by spiritual mentors at every meeting or service, which allows us to conclude that faith in Islam stands guard over the Day of Judgment, which will become a holiday for the true believer.

Including Lifetime Hydraulic Turbine Cost into Short-Term Hybrid Scheduling of Hydro and Solar

In traditional short-term hydropower scheduling problems, which usually determine the optimal power generation schedules within one week, the off-design zone of a hydraulic turbine is modeled as a forbidden zone due to the significantly increased risk of turbine damage when operating within this zone. However, it is still plausible to occasionally operate within this zone for short durations under real-world circumstances. With the integration of Variable Renewable Energy (VRE) into the power system, hydropower, as a dispatchable energy resource, operates complementarily with VRE to smooth overall power generation and enhance system performance. The rapid and frequent adjustments in output power make it inevitable for the hydraulic turbine to operate in the off-design zone. This paper introduces the operating zones associated with various production costs derived from fatigue analysis of the hydraulic turbine and calculated based on the turbine replacement cost. These costs are incorporated into a short-term hybrid scheduling tool based on Mixed Integer Linear Programming (MILP). Including production costs in the optimization problem shifts the turbine’s working point from a high-cost zone to a low-cost zone. The resulting production schedule for a Hydro-Solar hybrid power system considers not only short-term economic factors such as day-ahead market prices and water value but also lifetime hydraulic turbine cost, leading to a more comprehensive calculation of the production plan. This research provides valuable insights into the sustainable operation of hydropower plants, balancing short-term profits with lifetime hydraulic turbine costs.

A Novel NLP-based Stock Market Price Prediction and Risk Analysis Framework

Stock market prediction is an interesting and complex problem that has recently been in the limelight, thanks to the significant accuracy achieved by deep learning models. However, a complete platform with prediction and risk analysis ability is unavailable. In the current work, we present a novel framework for investment analysis designed to create ease for investors and provide a confidence measure along with the stock price to depict the risk involved in investing in stocks of a particular company. The model integrates two different approaches successfully to improve accuracy significantly. The model inputs two sources – a stock price dataset depicting the original scores as numerals and textual data extracted from Reddit news articles. The traditional problem of stock price prediction is dealt with using LSTMs on individual stock prices. At the same time, the confidence is represented by a risk value calculated intelligently using XGBoost and LSTM output. We have deployed natural language processing techniques for performing sentiment and subjectivity analyses, which are then used to extract features for further investigation in the study. The results show that an accuracy of 94% for stock trend prediction can be achieved using PCA as the feature extractor with tuned parameters for XGBoost and around 76% accuracy for stock price prediction with a tuned LSTM. It removes the hassle for investors to research the project or company they want to invest in and provides all relevant analysis and data.

Экология техносферы человеческого сообщества

In the 19th century, the sphere of interest of the science of “ecology” was formulated. Over the course of a century and a half, the semantics of its tasks has undergone significant changes. However, the original definition of ecology given by E.G. Haeckel, as a science about the interaction of living and inanimate nature, has again become relevant with the technological development of mankind. This development led to the emergence of an alternative habitat - the technosphere. The technosphere, within the framework of environmental ideas, is considered as a formation deeply hostile to nature in its essence. Nature is considered as a certain ideal that creates favorable conditions for the existence of living organisms in it. The experience of natural disasters and cataclysms, accompanied by the mass death of living organisms, indicates that this is an idealized idea. The existence of modern man outside the technosphere is practically impossible. The techno-sphere takes on the task of providing humanity with protection from pathogens. An example of such control is the use of DDT against vectors of malaria and other diseases. The technosphere solves problems in relation to humanity that are similar to providing the body with food, solving the problems of energy supply and movement over various distances – both people and cargo. The technosphere is evolving, using the achievements of developing sciences and technologies, reducing the specific impact on the natural environment while meeting the needs of the individual. For the further development of human ecology at the intersection of natural and artificial, the solution of traditional management problems is required: strict control of raw materials, technologies and products used, and their rational distribution. This is important since the human population is steadily growing and a significant part of the planet’s population finds itself in conditions of food and energy shortages, while the other part has an abundance of resources.

Optimizing Virtual Network Embedding for Avionics with Time-Triggered Traffic Scheduling

Network virtualization technology abstracts the nodes and links resources in the physical network, and enables multiple virtual networks (VNs) to share the substrate network (SN) resources through the Virtual Netw ork Embedding (VNE) method. For time-triggered Ethernet (TTE) used in avionics, a time-triggered traffic scheduling oriented virtual network embedding (TT-VNE) method is proposed. While meeting the total resource constraints of traditional VNE problem, it ensures the strict periodicity of time-triggered (TT) traffic. During the solution process, the method sorts the virtual nodes according to the TT traffic bandwidth requirements and the total bandwidth requirements of the links connected to the virtual nodes, embeds the virtual nodes using the breadth first search algorithm, and plans the virtual links in candidate shortest path aggregation. If the TT traffic in the current virtual link is not schedulable, the iterative design of local virtual node re embedding and path planning is carried out. The simulation shows that the request acceptance rate of TT-VNE is not lower than that of existing methods, and when the number of virtual network requests in the star topology exceeds 30 , its request acceptance rate is about 14.3%higher than the VNE-NTANRC-D method which only considers the network topology and resource attributes.

A multi-objective optimization scheduling approach of integrated energy system considering the exergy efficiency using the variable step-size approximation method

As an important part of the new power system, it is crucial to consider the efficiency, economy and environment of the integrated energy system simultaneously. In order to improve the efficiency of solving multi-objective optimization scheduling problems for integrated energy systems, a variable step-size approximation method is proposed in this paper. Firstly, a model of integrated energy system is constructed, and the source-side and load-side exergy equations of different energy networks are given, as well as the mathematical expressions of equipment by combining the idea of unification. Secondly, in comparison with the ergodic weights approach, the variable step-size approximation method theoretical minimum reduction percentage of solution time is deduced by mathematical methodology under the m objective functions. Finally, the results from an integrated energy system including a modified New England 30-bus system, a 6-node heat network and a 7-node gas network demonstrate that the multi-objective function proposed compromises the economy, environment and efficiency of integrated energy system compared to the traditional optimization problem. In comparison with the ergodic weights approach, the step size approximation method can save over 97 % of the solution time, and the percentage of average error can be as low as 0 % under the multi-objective functions.

Study of ZnO-SAE50 over a radiated permeable exponentially elongating curved device subject to non-uniform thermal source and Newtonian heating

Thermal analysis in nanolubricants is a topic of interest. The nanolubricants are uniform mixture of nanoparticles and oil. These fluids extensively use for thermal applications and friction reduction. Hence, the key objectives of this work to analyze the dynamics of ZnO-SAE50 through a curved surface. The traditional problem modified using novel effects of non-uniform thermal source, variable thermal conductivity, non-linear thermal radiations and Newtonian heating in the presence of convective condition. Further, enhanced properties of ZnO-SAE50 used to achieve the desired results. The physical results for the problem obtained through numerical approach (RK scheme coupled with shooting method). It is examined that the velocity enhances by increasing the curvature of the surface (K=5.0,10.0,15.0,20.0) while stronger Hartmann effects (M=0.1,0.5,0.9,1.3) and concentration of ZnO reduce the motion over the surface. The momentum boundary layer declines for S>0 as compared to S<0. Inclusions of non-uniform source enhanced the thermal transport while prominent increase is observed for Biot number (Bi=0.1,0.2,0.3,0.4). Further, the variable thermal conductivity (ϵ=0.1,0.2,0.3,0.4) and Newtonian heating have minimal variations in the temperature. Thermal radiations and ϕ improve the temperature of ZnO-SAE50. Thus, modified problem in the presence of indicated physical phenomenon has effective outcomes regarding the heat transfer applications in nanolubricants.

Approximating decision trees with priority hypotheses

This paper addresses the problem of creating decision trees for identifying hypotheses, also known as entities, in a setting where the cost of an action is dependent on the true hypothesis. Specifically, we consider the scenario where n hypotheses are divided into m groups based on their priority levels. Taking an action on a higher priority hypothesis incurs a higher cost. This is relevant to many real-world applications where cost-sensitive decisions need to be made. For example, in a medical diagnosis task, the goal is to take a series of actions (such as medical tests) to identify a cause. Each action in this process requires conducting a test on the patient and observing the outcome, which can take anywhere from a few minutes to several weeks depending on the test. In this case, the cost (the result of waiting for the outcome) is higher if the true hypothesis is more time-sensitive. For example, if the true hypothesis is toxic chemical exposure (as opposed to a chronic disease such as diabetes), a delay of a few minutes could significantly increase the patient's risk of mortality. We propose a group greedy algorithm to solve this problem. We demonstrate that under worst-case scenarios, our algorithm has an approximation ratio of O(mlog⁡n). Importantly, when m=1, meaning there is only one group of hypotheses, our result is consistent with the logarithmic approximation bound for the traditional optimal decision tree problem.

Dynamic strain field estimation of fixed offshore support structures using limited acceleration measurements

The strain history affecting the fatigue life of fixed offshore support structures is one of the most important topics in structural monitoring. However, some critical fatigue locations are located below the seabed, and the installation of strain gauges at these locations entails significant maintenance costs. In contrast, acceleration which reflects the vibration characteristics of the structure is an easily accessible and more stable physical parameter. Therefore, this study presents a strain estimation scheme that uses limited acceleration measurements to estimate the dynamic strain field of the fixed offshore support structures. An adaptive displacement reconstruction method is used to obtain the displacements at the master degrees of freedom, then the global displacements are obtained by the evolutionary orthogonal response expansion method, and finally, these displacements are implemented into the numerical software by redefining the boundary conditions to obtain the dynamic strain field. The advantage of the proposed method over the traditional strain-solving forward problem is that it relies solely on measured acceleration and structural geometry information, without requiring displacement, strain, or modal information. The correctness of the proposed method is verified by a numerical jacket platform model with multi-harmonic and non-stationary random excitations. Furthermore, physical experiments under eccentric rotation and hammering excitations are performed to assess the applicability of the proposed method on a wind turbine model equipped with laser displacement and fibre bragg grating sensors as reference measurements. The results show that the proposed method can accurately predict the dynamic strains of jacket platform and wind turbine support structures, while the appropriate agreement between the estimated strains and the numerical results, and the optical measured response is observed, with maximum estimation errors of 1.80% and 4.63%, respectively. Summarily, the application of the approach enables a new type of strain monitoring applicable to fixed offshore support structures.

The hectometric modelling challenge: Gaps in the current state of the art and ways forward towards the implementation of 100‐m scale weather and climate models

AbstractFor a number of years research has been carried out in several centres which has demonstrated the potential benefits of 100‐m scale models for a range of meteorological phenomena. More recently, some meteorological services have started to consider seriously the operational implementation of practical hectometric models. Many, but by no means all, of the applications are likely to relate to urban areas, where the enhanced resolution has obvious benefits. This article is concerned with the issues that need to be addressed to bridge the gap between research at 100‐m scales and practical models. We highlight a number of key issues that need to be addressed, with suggestions of important avenues for future development. An overarching issue is the high computational cost of these models. Although some ideas to reduce this are presented, it will always be a serious constraint. This means that the benefits of these models over lower resolution ones, or other techniques for generating high‐resolution forecasts, will need to be clearly understood, as will the trade‐offs with resolution. We discuss issues with model dynamical cores and physics–dynamics coupling. There are a number of challenges around model parameterisations, where some of the traditional problems (e.g., convection) become easier but a number of new challenges (e.g., around surface parameterisations) appear. Observational data at these scales present a challenge and novel types of observations will need to be considered. Data assimilation will be needed for short‐range forecasts, but there is currently little knowledge of this, although some of the likely issues are clear. An ensemble approach will be essential in many cases (e.g., convection), but research is needed into ensembles at these scales and significant work on post‐processing systems is required to make the best use of models at these grid lengths.

A large-scale group decision-making framework based on two-dimensional picture fuzzy sets in the selection of optimal carbon emission reduction alternatives

This article proposes the concept of two-dimensional picture fuzzy sets (TDPFS). This new framework extends the original picture fuzzy sets (PFS) by incorporating a confidence level to evaluate expert opinions using probability. This innovation facilitates the stratification and dimensionality reduction of large-scale expert groups, while also offering a new perspective for examining group decision-making problems. This article determines the weights of each subgroup by considering the confidence level of expert evaluation, the cohesion of the subgroups, and the size of the subgroups. This approach simplifies the decision-making process by transforming the originally complex large-scale group decision-making problem into a traditional decision-making problem. Finally, the traditional consensus reaching process (CRP) ensures that experts meet specific consensus requirements in decision-making. To verify the feasibility and practicality of the framework in practical applications, we applied it to the selection of carbon emission reduction paths and alternatives in China’s iron and steel industry. This practice not only demonstrates the theoretical value of our research results but also highlights its potential in practical operations.

Beyond Morbidity and Mortality Conference: How Do We Learn From Special Cases?

Physicians experience scary cases in the course of usual medical practice. Cases of near misses, legal and ethical dilemmas, or unique clinical challenges are great sources of education. However, there is no format for presentation and dissemination of cases that do not meet criteria for morbidity and mortality (M&M) conferences. The Scary Cases Conference is an innovative educational forum for scrutiny and analysis of these challenging clinical cases. Scary Cases differs from traditional Morbidity and Mortality conferences as it explores outcomes beyond the scope of medical or surgical errors. From 2011 to 2021: 11 regional and 10 national "Otolaryngology Scary Cases" conferences and mini-seminars were held. The cases presented were analyzed for case specialty, area of management deemed troublesome, and compared to M&M conference criteria. 187 cases were presented. 62% percent of cases included traditional medical problems, whereas 21% involved legal issues, and 17% focused on ethical dilemmas. For the cases with medical problems, 31% involved airway obstruction, 17% nerve injuries, and 17% malignancy. 49% of cases would have met criteria for presentation at traditional M&M conferences. Of all the "scary cases," 25% were near misses and 26% represented ethical or legal dilemmas which would not be classified as morbitidy, mortality, or near miss. The Scary Cases provides a forum conducive to learning amongst peers and experts. It allows medical and surgical specialties to share the cases deemed most impactful. The M&M conference would only include half of such cases, but could be expanded beyond the traditional scope in the future.